User Intentions or "The other end of the camera ..."

User intentions in
multimedia or “The other
end of the camera …”

Mathias Lux
Klagenfurt University
mlux@itec.uni-klu.ac.at

Research Areas &
Connections http://www.uni-klu.ac.at

2

More & More Questions …
http://www.uni-klu.ac.at

All my previous work led into one single direction:
● What does the user actually want and how to
support the user in her/his work with the
computer?

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User centered computing

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Context-Awareness

[…] context-aware software adapts according to
the location of use, the collection of nearby
people, hosts, and accessible devices, as well
as to changes to such things over time […]

Src. B. Schilit, N. Adams, and R. Want. (1994). "Context-aware computing applications"
(PDF). IEEE Workshop on Mobile Computing Systems and Applications
(WMCSA'94), Santa Cruz, CA, US. pp. 89-101.

5

Context …

● Is a broad concept
● Can be defined in different ways
o depending on domain
o depending on application

Idea: pick out most promising piece of the
„context“ and take a look at it!

6

User Intentions

● Users have certain intentions
o … doing something intentional …

Hypothesis: If I know the intention of a user
beforehand, I can better support her/his actions.

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User Intentions: Example

A user wants to buy a car, but there is no pressing
need.
● The users intention is “buy a car”
● The actions resulting from the intention might not
be directed and planned
o “Oh, there is a car I like …”
o “I’ve heard you’re going to sell your Prius…”

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User Intentions

● Intentions are fuzzy and vague
o Hard to measure …

● Concept of “user goals”
o .. state of affair that a user wants to achieve …
o Can be measured: (not or partially) achieved

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User Goals: Example

A user wants to buy a car, but there is no pressing
need.
● The users intention is “buy a car”
● Possible goal “find car that fits the users needs”
o Task: Searching for a car with specific characteristics
o End of the task: Car found

=> Goals are very specific

10

Agenda

● Motivation & Introduction
● User goals in text retrieval
● User goals in digital photo retrieval
● User goals & intentions in media production
● Outlook

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A Taxonomy of Web Search

● Navigational
o The immediate intent is to reach a particular site
● Informational
o The intent is to acquire some information assumed to
be present on one or more web pages
● Transactional
o The intent is to perform some web-mediated activity

Src. A. Broder, A Taxonomy of Web Search, ACM SIGIR Forum Vol 36, Issue
2, Fall 2002

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Revised Taxonomy

● Navigational
● Informational
o Directed, Undirected, Advice, Locate, List
● Resource
o Download, Entertain, Interact, Obtain

Src. Rose, D., Levinson, D., Understanding user goals in web search, Proc.
WWW 2004, New York, USA (2004).

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User Goals in Web Search

Src. Rose, D., Levinson, D., Understanding user goals in web search, Proc.
WWW 2004, New York, USA (2004).

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How do users express
goals? http://www.uni-klu.ac.at

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Degrees of Explicitness in
Intentional Artifacts http://www.uni-klu.ac.at

● How can we find explicit goals of users?
● How can search queries be classified as explicit
intentional queries?
● Explicit goals vs. implicit goals

Example: car, car Miami, car Miami dealer, buy a car in
Miami, buy a used car in Miami, get loan to buy a used
car in Miami

Src. Strohmaier, Prettenhofer & Lux, Different Degrees of Explicitness in Intentional Artifacts: Studying User Goals in a
Large Search Query Log, SKGOI'08 @ IUI'08, Canary Islands, Spain, 2008

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Degrees of Explicitness in
Intentional Artifacts http://www.uni-klu.ac.at

● Experimental classification
o Part of speech tagging on queries
o Naïve Bayes classifier on
• 98 instances
(59 pos. & 39 neg.)

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Classifier results …

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Website Shares in
Condensed Data Set http://www.uni-klu.ac.at

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ehow.com

Powered by demand media:
● Mining questions from queries
● Pay people for answers
o 20$ per video,
o 2 ½ $ copy-edit,
o 1$ fact-check …

see also http://www.wired.com/magazine/2009/10/ff_demandmedia/

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Agenda

● Outlook

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How do users express their
goals on … say Flickr ;) http://www.uni-klu.ac.at

● Queries for photo search are short
o “dog dachshund bark” rather than
o “image showing a small dog, preferably a dachshund,
barking for use in a brochure”
● Hypothesis: User goals affect the search and
browsing behavior of users
o Click-through rate, session time,
medium click interval, etc.

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Methodology

Exploratory study on user goal classification
● Definition of tasks reflecting different types of
goals
o Interviews with experts using image search
o Note: Verification of relevance of tasks needed
● Presentation of goals to users in a study
o Recording of search behavior
o Analysis of possible features for classification

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Study setup

● Taxonomy of Broder / Rose & Levinson
● 10 tasks from different classes
o Find picture expressing joy
o Find picture of the Eiffel Tower
o Find picture taken with a Canon IXUS 980 IS
o Find out how to tie a tie
o …

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Findings

● Revised taxonomy needed
● Classification difficult …
o where do session start and end?
o fuzzy transition between goals?
o Dependencies between goals, subgoals etc.?
● Classification prototype
o Rule based
o Adapting results view

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Taxonomy development

● Several additional studies
o Expert & non expert users
● Revised taxonomy
● Feature selection for classification

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Agenda

● Outlook

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Intentions in Media
Production http://www.uni-klu.ac.at

● Annotation tool for digital photos
o done by two amateur photographers
● Two different roles
o Creator
o Consumer
● Study:
1. How do users get along with the UI
2. How do users get along with intentions for
annotation

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iPan: Intention-based
Photo Annotation http://www.uni-klu.ac.at

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iPan preliminary Results

● Tool tested in photographers user group
o 2 extreme types: intentional photos and non-
intentional photos
o Maybe artists, who want to hide intentions?
● Interviews have been rather discouraging
o Mainly no intention to use such a tool
o No understanding for “intentional photography”
o One possible user => more like storytelling

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Ongoing work …

● Annotation tool based on intentions
● Taxonomy of goals in media production
● Investigation for other media

31

Agenda

● Outlook

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Users in a “near time” MMIS

● Assume there is a big “Ironman” event
o sequence of
• 3.86 km of swimming,
• 180.2 km of biking and
• 42.195 km of running
o like Klagenfurt in 2007:
• 2,400 participants with support team (3-4 people)
• 2,000 volunteers
• 100,000 visitors
• 6 moderators & DJs / 3 video walls
• event lasted end to end about 17 hours

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Users in a “near time” MMIS

● Users have different roles
o Participants, support, guards, journalists …
● Users have different intentions
o I want to track athlete XY
o I want to track the lead
o I want to follow events
● User participate in a
“social MMIS”

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Intentions in a “near time” MMIS

● User intentions can be made explicit
o Classification, user feedback, context, etc.
● Intentions & goals can be leveraged to enhance
retrieval and visualization of content
o Relevance function (cp. popularity, 80:20)
o Abstraction & summarization
o Pro-active distribution

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Summary & Conclusion

User intentions
● … have not yet been explored in MIR & MMIS
● … may help bridging the semantic gap (from the
other side)
● … may help dealing with the “long tail”

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Thanks …

… for your time

and: I’d be happy to discuss
the whole thing

Mathias Lux
mlux@itec.uni-klu.ac.at

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User Intentions or "The other end of the camera ..."

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